WO2023249517A1 - Estimation de la qualité de canal de liaison descendante (dl) attendue et incertitude associée à utiliser dans une adaptation de liaison - Google Patents

Estimation de la qualité de canal de liaison descendante (dl) attendue et incertitude associée à utiliser dans une adaptation de liaison Download PDF

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Publication number
WO2023249517A1
WO2023249517A1 PCT/SE2022/050601 SE2022050601W WO2023249517A1 WO 2023249517 A1 WO2023249517 A1 WO 2023249517A1 SE 2022050601 W SE2022050601 W SE 2022050601W WO 2023249517 A1 WO2023249517 A1 WO 2023249517A1
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WIPO (PCT)
Prior art keywords
channel quality
rank
base
network node
mean
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PCT/SE2022/050601
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English (en)
Inventor
Jonas FRÖBERG OLSSON
Erik Eriksson
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Telefonaktiebolaget Lm Ericsson (Publ)
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Application filed by Telefonaktiebolaget Lm Ericsson (Publ) filed Critical Telefonaktiebolaget Lm Ericsson (Publ)
Priority to PCT/SE2022/050601 priority Critical patent/WO2023249517A1/fr
Publication of WO2023249517A1 publication Critical patent/WO2023249517A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0053—Allocation of signaling, i.e. of overhead other than pilot signals
    • H04L5/0057—Physical resource allocation for CQI
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/063—Parameters other than those covered in groups H04B7/0623 - H04B7/0634, e.g. channel matrix rank or transmit mode selection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0632—Channel quality parameters, e.g. channel quality indicator [CQI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0636—Feedback format
    • H04B7/0645—Variable feedback
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
    • H04L1/0002—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the transmission rate
    • H04L1/0003—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the transmission rate by switching between different modulation schemes
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
    • H04L1/0009—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the channel coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
    • H04L1/0023—Systems modifying transmission characteristics according to link quality, e.g. power backoff characterised by the signalling
    • H04L1/0026—Transmission of channel quality indication

Definitions

  • the present disclosure relates to wireless communications, and in particular, to estimating an expected downlink channel quality, such as an expected signal to interference plus noise ratio (SINR), and associated uncertainty for use in link adaptation.
  • SINR signal to interference plus noise ratio
  • the scheduler in a NR base station hereafter referred to as a network node, is responsible for resource allocation for WDs in a connected mode in both uplink and downlink communications.
  • the scheduler receives from the core the input related to the required quality of service (QoS) for each WD and service.
  • QoS quality of service
  • the scheduler works closely with a link adaptation (LA) process to select the proper transport block formats for uplink and downlink transmissions.
  • the LA process determines a radio resource assignment for a WD based on estimated signal to interference plus noise ratio (SINR), the outcome of WD’s previous transmission, as communicated from the WD to the network node in an acknowledgement/non- acknowledgement (ACK/NACK), a WD’s power headroom, and the available bandwidth.
  • SINR estimated signal to interference plus noise ratio
  • FIG. 1 illustrates an example of interaction between a scheduler 2, a QoS unit 4 in a core network node, a link adapter 6 and a power control unit 8 in the network node (gNB).
  • the scheduler 2 and link adapter 6 have the task of selecting a transport format that includes a resource allocation, a number of layers, and a modulation and coding scheme (MCS) for a downlink transmission based on channel state information (CSI) from the WD.
  • MCS modulation and coding scheme
  • CSI channel state information
  • the selection is based on CSI measured by the network node. For services with high reliability requirements, this can be a challenging task, especially if the transport format also should be spectrally efficient.
  • a fundamental problem with link adaption is that in general it is impossible for the network node to determine a predicted SINR that is always the same as the SINR that the transmission will experience. Hence, to any predicted SINR there is an associated uncertainty.
  • Traditional link adaptation mitigates this uncertainty by relying on hybrid automatic request (HARQ) re-transmissions.
  • HARQ hybrid automatic request
  • the number of HARQ re-transmissions that can be performed may be rather few, which means that the link adaptation process should account for the uncertainty in the predicted SINR based on measured SINR.
  • the uncertainty in the predicted SINR may depend on several factors which may include:
  • downlink (DL) and uplink (UL) channels may be assumed to be reciprocal.
  • the DL channel H can be estimated in the network node from SRS (Sounding Reference Signals) measurements.
  • SRS Sounding Reference Signals
  • the network node may choose from an infinite set of precoders, instead of using the pre-coder suggested by the WD, (where the suggested precoder is deduced by the WD from a finite set of pre-coders in a pre-coder codebook.)
  • the network node cannot directly determine SINR from H since the network node does not know the noise and interference covariance matrix Q. Hence, CSI reports are still needed by the network node to produce an estimate of Q.
  • the covariance matrix Q may be estimated in different ways.
  • SINR(l) for layer I is determined according to the following formula if the receiver is a minimum mean squared error interference rejection combining (MMSE-IRC) receiver: (1) where * denotes Hermitian conjugate, and A(l, Z) denotes the Z-th diagonal element.
  • MMSE-IRC minimum mean squared error interference rejection combining
  • P is the pre-coder reported by the WD while H is the channel network node determined from the sounding reference signal (SRS).
  • SRS sounding reference signal
  • Another problem is how to select a target BLEP in LA as it is known that a target BLEP of 10% is generally not optimal.
  • Some embodiments advantageously provide methods, systems, and apparatuses for estimating an expected downlink signal to interference ratio and associated uncertainty for use in link adaptation.
  • a method may include one or more of the following: transforming reported channel quality for a given rank to a domain independent of rank; determining of statistical measures such as mean, variance or standard deviation (std), and time auto-correlation in the rank-independent domain; and/or determining rank-specific estimates of expected channel quality (such as expected SINR) and associated uncertainty based on a latest received CSI report and determined statistical measures.
  • a method may include one or more of the following: transforming reported channel quality for a given rank to a domain independent of rank; determination of statistical measures such as mean, variance/std, and time auto-correlation in the rank-independent domain; and/or determining rank-specific estimates of expected channel quality and associated uncertainty based on latest received CSI report and determined statistical measures.
  • a method in a network node configured to communicate with a wireless device, WD includes mapping each of a plurality of channel quality values according to a first mapping function, the first mapping function being based at least in part on a base rank and a second rank.
  • the method also includes determining a base mean and a base variance of the mapped channel quality values.
  • the method further includes, given a first mapped channel quality value of the mapped channel quality values, determining a second mean and a second variance based at least in part on the base mean, the base variance, the first mapped channel quality value and an autocorrelation of the mapped channel quality values.
  • the method also includes mapping the second mean according to a second mapping function, the second mapping function being based at least in part on the base rank and a selected rank, to determine an estimated channel quality value expected at a future time for the selected rank.
  • the method further includes measuring the plurality of channel quality values at successive times based at least in part on a rank reported by the WD and a channel quality index, CQI, reported by the WD.
  • the plurality of measured channel quality values are filtered prior to determining the base mean and the base variance.
  • the plurality of measured channel quality values are weighted to give greater weight to more recent measured channel quality values than weight given to less recent measured channel quality values.
  • determining the second mean and the second variance further includes interpolating the autocorrelation of the mapped channel quality values.
  • the method also includes determining the plurality of channel quality values based at least in part on a random variable.
  • determining the base mean and the base variance is based at least in part on determining a mean and variance for each of a plurality of sub-bands.
  • the second rank is one of a rank intended for downlink transmission and a rank last reported by the WD, and wherein the selected rank is the rank intended for downlink transmission.
  • a channel quality value is a signal to interference plus noise ratio, SINR.
  • the processing circuitry is further configured to compare the second variance to a first threshold, the first threshold being associated with a maximum block error probability, BLEP. In some embodiments, the processing circuitry is further configured to compare the second variance to a second threshold, the second threshold being associated with a minimum block error probability, BLEP.
  • the second mapping function is based at least in part on an inverse of the first mapping function. In some embodiments, the first mapping function includes: where Q is a measured channel quality value, RIbase is a base rank indicator and RI is a reported rank indicator, kiu is a constant selected based at least in part on whether the base rank is greater than the second rank, and where SINR is a measured SINR.
  • the processing circuitry is further configured to measure the plurality of channel quality values at successive times based at least in part on a rank reported by the WD and a channel quality index, CQI, reported by the WD.
  • the plurality of measured channel quality values are filtered prior to determining the base mean and the base variance.
  • the plurality of measured channel quality values are weighted to give greater weight to more recent measured channel quality values than weight given to less recent measured channel quality values.
  • determining the second mean and the second variance further includes interpolating the autocorrelation of the mapped channel quality values.
  • the processing circuitry is further configured to determine the plurality of channel quality values based at least in part on a random variable.
  • determining the base mean and the base variance is based at least in part on determining a mean and variance for each a plurality of subbands.
  • the second rank is one of a rank intended for downlink transmission and a rank last reported by the WD, and wherein the selected rank is the rank intended for downlink transmission.
  • a channel quality value is a signal to interference plus noise ratio, SINR.
  • FIG. 4 is a flowchart illustrating exemplary methods implemented in a communication system including a host computer, a network node and a wireless device for executing a client application at a wireless device according to some embodiments of the present disclosure
  • FIG. 7 is a flowchart illustrating exemplary methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a host computer according to some embodiments of the present disclosure.
  • network node can be any kind of network node comprised in a radio network which may further comprise any of base station (BS), radio base station, base transceiver station (BTS), base station controller (BSC), radio network controller (RNC), g Node B (gNB), evolved Node B (eNB or eNodeB), Node B, multi-standard radio (MSR) radio node such as MSR BS, multi-cell/multicast coordination entity (MCE), integrated access and backhaul (IAB) node, relay node, donor node controlling relay, radio access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU) Remote Radio Head (RRH), a core network node (e.g., mobile management entity (MME), self-organizing network (SON) node, a coordinating node, positioning node, MDT node, etc.), an external node (e.g., 3rd party node, a node external to the current network), nodes in distributed antenna system (
  • BS base station
  • the WD may also be a radio communication device, target device, device to device (D2D) WD, machine type WD or WD capable of machine to machine communication (M2M), low-cost and/or low-complexity WD, a sensor equipped with WD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, Customer Premises Equipment (CPE), an Internet of Things (loT) device, or a Narrowband loT (NB-IOT) device, etc.
  • D2D device to device
  • M2M machine to machine communication
  • M2M machine to machine communication
  • Tablet mobile terminals
  • smart phone laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles
  • CPE Customer Premises Equipment
  • LME Customer Premises Equipment
  • NB-IOT Narrowband loT
  • wireless devices such as, for example, 3GPP LTE and/or New Radio (NR)
  • WCDMA Wide Band Code Division Multiple Access
  • WiMax Worldwide Interoperability for Microwave Access
  • UMB Ultra Mobile Broadband
  • GSM Global System for Mobile Communications
  • functions described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and/or network nodes. In other words, it is contemplated that the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.
  • Each network node 16a, 16b, 16c is connectable to the core network 14 over a wired or wireless connection 20.
  • a first wireless device (WD) 22a located in coverage area 18a is configured to wirelessly connect to, or be paged by, the corresponding network node 16a.
  • a second WD 22b in coverage area 18b is wirelessly connectable to the corresponding network node 16b. While a plurality of WDs 22a, 22b (collectively referred to as wireless devices 22) are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole WD is in the coverage area or where a sole WD is connecting to the corresponding network node 16. Note that although only two WDs 22 and three network nodes 16 are shown for convenience, the communication system may include many more WDs 22 and network nodes 16.
  • a WD 22 can be in simultaneous communication and/or configured to separately communicate with more than one network node 16 and more than one type of network node 16.
  • a WD 22 can have dual connectivity with a network node 16 that supports LTE and the same or a different network node 16 that supports NR.
  • WD 22 can be in communication with an eNB for LTE/E-UTRAN and a gNB for NR/NG-RAN.
  • the communication system 10 may itself be connected to a host computer 24, which may be embodied in the hardware and/or software of a standalone server, a cloud-implemented server, a distributed server or as processing resources in a server farm.
  • the host computer 24 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider.
  • the connections 26, 28 between the communication system 10 and the host computer 24 may extend directly from the core network 14 to the host computer 24 or may extend via an optional intermediate network 30.
  • the intermediate network 30 may be one of, or a combination of more than one of, a public, private or hosted network.
  • the intermediate network 30, if any, may be a backbone network or the Internet. In some embodiments, the intermediate network 30 may comprise two or more sub-networks (not shown).
  • the communication system of FIG. 2 as a whole enables connectivity between one of the connected WDs 22a, 22b and the host computer 24.
  • the connectivity may be described as an over-the-top (OTT) connection.
  • the host computer 24 and the connected WDs 22a, 22b are configured to communicate data and/or signaling via the OTT connection, using the access network 12, the core network 14, any intermediate network 30 and possible further infrastructure (not shown) as intermediaries.
  • the OTT connection may be transparent in the sense that at least some of the participating communication devices through which the OTT connection passes are unaware of routing of uplink and downlink communications.
  • a network node 16 is configured to include a mapping unit 32 which may be configured to map each of a plurality of channel quality values according to a first mapping function, the first mapping function being based at least in part on a base rank and a second rank.
  • the network node may also include a statistics unit 34 which is configured to determine a base mean and a base variance of the mapped channel quality values.
  • the mapping unit 32 may also be configured to map a second mean according to a second mapping function, the second mapping function being based on the base rank and a selected rank, to determine an estimated channel quality value expected at a future time for the selected rank.
  • the statistics unit 34 may also be configured to, given a first mapped channel quality value of the mapped channel quality values, determine a second mean and a second variance based at least in part on the base mean, the base variance, the first mapped channel quality value and an autocorrelation of the mapped channel quality values.
  • a host computer 24 comprises hardware (HW) 38 including a communication interface 40 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of the communication system 10.
  • the host computer 24 further comprises processing circuitry 42, which may have storage and/or processing capabilities.
  • the processing circuitry 42 may include a processor 44 and memory 46.
  • the processor 44 may be configured to access (e.g., write to and/or read from) memory 46, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read- Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • memory 46 may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read- Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • Processing circuitry 42 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by host computer 24.
  • Processor 44 corresponds to one or more processors 44 for performing host computer 24 functions described herein.
  • the host computer 24 includes memory 46 that is configured to store data, programmatic software code and/or other information described herein.
  • the software 48 and/or the host application 50 may include instructions that, when executed by the processor 44 and/or processing circuitry 42, causes the processor 44 and/or processing circuitry 42 to perform the processes described herein with respect to host computer 24.
  • the instructions may be software associated with the host computer 24.
  • the software 48 may be executable by the processing circuitry 42.
  • the software 48 includes a host application 50.
  • the host application 50 may be operable to provide a service to a remote user, such as a WD 22 connecting via an OTT connection 52 terminating at the WD 22 and the host computer 24.
  • the host application 50 may provide user data which is transmitted using the OTT connection 52.
  • the “user data” may be data and information described herein as implementing the described functionality.
  • the host computer 24 may be configured for providing control and functionality to a service provider and may be operated by the service provider or on behalf of the service provider.
  • the processing circuitry 42 of the host computer 24 may enable the host computer 24 to observe, monitor, control, transmit to and/or receive from the network node 16 and or the wireless device 22.
  • the communication system 10 further includes a network node 16 provided in a communication system 10 and including hardware 58 enabling it to communicate with the host computer 24 and with the WD 22.
  • the hardware 58 may include a communication interface 60 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of the communication system 10, as well as a radio interface 62 for setting up and maintaining at least a wireless connection 64 with a WD 22 located in a coverage area 18 served by the network node 16.
  • the radio interface 62 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.
  • the communication interface 60 may be configured to facilitate a connection 66 to the host computer 24.
  • the connection 66 may be direct or it may pass through a core network 14 of the communication system 10 and/or through one or more intermediate networks 30 outside the communication system 10.
  • the hardware 58 of the network node 16 further includes processing circuitry 68.
  • the processing circuitry 68 may include a processor 70 and a memory 72.
  • the processing circuitry 68 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions.
  • FPGAs Field Programmable Gate Array
  • ASICs Application Specific Integrated Circuitry
  • the processor 70 may be configured to access (e.g., write to and/or read from) the memory 72, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • volatile and/or nonvolatile memory e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • the network node 16 further has software 74 stored internally in, for example, memory 72, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the network node 16 via an external connection.
  • the software 74 may be executable by the processing circuitry 68.
  • the processing circuitry 68 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by network node 16.
  • Processor 70 corresponds to one or more processors 70 for performing network node 16 functions described herein.
  • the memory 72 is configured to store data, programmatic software code and/or other information described herein.
  • the software 74 may include instructions that, when executed by the processor 70 and/or processing circuitry 68, causes the processor 70 and/or processing circuitry 68 to perform the processes described herein with respect to network node 16.
  • processing circuitry 68 of the network node 16 may include a mapping unit 32 which may be configured to map each of a plurality of channel quality values, according to a first mapping function, the first mapping function being based at least in part on a base rank and a second rank.
  • the network node may also include a statistics unit 34 which is configured to determine a base mean and a base variance of the mapped channel quality values.
  • the mapping unit 32 may also be configured to map a second mean according to a second mapping function, the second mapping function being based on the base rank and a selected rank, to determine an estimated channel quality value expected at a future time for the selected rank.
  • the statistics unit 34 may also be configured to, given a first mapped channel quality value of the mapped channel quality values, determine a second mean and a second variance based at least in part on the base mean, the base variance, the first mapped channel quality value and an autocorrelation of the mapped channel quality values.
  • the communication system 10 further includes the WD 22 already referred to.
  • the WD 22 may have hardware 80 that may include a radio interface 82 configured to set up and maintain a wireless connection 64 with a network node 16 serving a coverage area 18 in which the WD 22 is currently located.
  • the radio interface 82 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.
  • the hardware 80 of the WD 22 further includes processing circuitry 84.
  • the processing circuitry 84 may include a processor 86 and memory 88.
  • the processing circuitry 84 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions.
  • the processor 86 may be configured to access (e.g., write to and/or read from) memory 88, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • memory 88 may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • the WD 22 may further comprise software 90, which is stored in, for example, memory 88 at the WD 22, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the WD 22.
  • the software 90 may be executable by the processing circuitry 84.
  • the software 90 may include a client application 92.
  • the client application 92 may be operable to provide a service to a human or non-human user via the WD 22, with the support of the host computer 24.
  • an executing host application 50 may communicate with the executing client application 92 via the OTT connection 52 terminating at the WD 22 and the host computer 24.
  • the client application 92 may receive request data from the host application 50 and provide user data in response to the request data.
  • the OTT connection 52 may transfer both the request data and the user data.
  • the client application 92 may interact with the user to generate the user data that it provides.
  • the processing circuitry 84 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by WD 22.
  • the processor 86 corresponds to one or more processors 86 for performing WD 22 functions described herein.
  • the WD 22 includes memory 88 that is configured to store data, programmatic software code and/or other information described herein.
  • the software 90 and/or the client application 92 may include instructions that, when executed by the processor 86 and/or processing circuitry 84, causes the processor 86 and/or processing circuitry 84 to perform the processes described herein with respect to WD 22.
  • the inner workings of the network node 16, WD 22, and host computer 24 may be as shown in FIG. 3 and independently, the surrounding network topology may be that of FIG. 2.
  • the OTT connection 52 has been drawn abstractly to illustrate the communication between the host computer 24 and the wireless device 22 via the network node 16, without explicit reference to any intermediary devices and the precise routing of messages via these devices.
  • Network infrastructure may determine the routing, which it may be configured to hide from the WD 22 or from the service provider operating the host computer 24, or both. While the OTT connection 52 is active, the network infrastructure may further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network).
  • the wireless connection 64 between the WD 22 and the network node 16 is in accordance with the teachings of the embodiments described throughout this disclosure.
  • One or more of the various embodiments improve the performance of OTT services provided to the WD 22 using the OTT connection 52, in which the wireless connection 64 may form the last segment. More precisely, the teachings of some of these embodiments may improve the data rate, latency, and/or power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, better responsiveness, extended battery lifetime, etc.
  • the host computer 24 includes processing circuitry 42 configured to provide user data and a communication interface 40 that is configured to forward the user data to a cellular network for transmission to the WD 22.
  • the cellular network also includes the network node 16 with a radio interface 62.
  • the network node 16 is configured to, and/or the network node’s 16 processing circuitry 68 is configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/ supporting/ending a transmission to the WD 22, and/or preparing/terminating/ maintaining/supporting/ending in receipt of a transmission from the WD 22.
  • the network node 16 transmits to the WD 22 the user data which was carried in the transmission that the host computer 24 initiated, in accordance with the teachings of the embodiments described throughout this disclosure (Block SI 06).
  • the WD 22 executes a client application, such as, for example, the client application 92, associated with the host application 50 executed by the host computer 24 (Block SI 08).
  • FIG. 5 is a flowchart illustrating an exemplary method implemented in a communication system, such as, for example, the communication system of FIG. 2, in accordance with one embodiment.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 2 and 3.
  • the host computer 24 provides user data (Block SI 10).
  • the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50.
  • the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block SI 12).
  • the transmission may pass via the network node 16, in accordance with the teachings of the embodiments described throughout this disclosure.
  • the WD 22 receives the user data carried in the transmission (Block SI 14).
  • FIG. 6 is a flowchart illustrating an exemplary method implemented in a communication system, such as, for example, the communication system of FIG. 2, in accordance with one embodiment.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 2 and 3.
  • the WD 22 receives input data provided by the host computer 24 (Block SI 16).
  • the WD 22 executes the client application 92, which provides the user data in reaction to the received input data provided by the host computer 24 (Block SI 18).
  • the WD 22 provides user data (Block S120).
  • the method includes comparing the second variance to a first threshold, the first threshold being associated with a maximum block error probability, BLEP. In some embodiments, the method also includes comparing the second variance to a second threshold, the second threshold being associated with a minimum block error probability, BLEP. In some embodiments, the second mapping function is based at least in part on an inverse of the first mapping function. In some embodiments, the first mapping function includes: where Q is a measured signal channel quality value, RIbase is a base rank indicator and RI is a reported rank indicator, kiu is a constant selected based at least in part on whether the base rank is greater than the second rank.
  • the method further includes measuring the plurality of channel quality values at successive times based at least in part on a rank reported by the WD and a channel quality index, CQI, reported by the WD.
  • the plurality of measured channel quality values are filtered prior to determining the base mean and the base variance.
  • the plurality of measured channel quality values are weighted to give greater weight to more recent measured channel quality values than weight given to less recent measured channel quality values.
  • determining the second mean and the second variance further includes interpolating the autocorrelation of the mapped channel quality values.
  • the method also includes determining the plurality of channel quality values based at least in part on a random variable.
  • determining the base mean and the base variance is based at least in part on determining a mean and variance for each of a plurality of sub-bands.
  • the second rank is one of a rank intended for downlink transmission and a rank last reported by the WD, and wherein the selected rank is the rank intended for downlink transmission.
  • a channel quality value is a signal to interference plus noise ratio, SINR.
  • Embodiment 0 Using expected SINR and SINR uncertainty in link adaptation
  • the expected SINR and SINR uncertainty is represented as the mean ⁇ and std ⁇ (or variance ⁇ 2 )
  • the expected SINR is the SINR that is predicted by calculations involving measurements of SINR.
  • Measurements of SINR or “SINR measurements” refer to SINR determined based at least in part on CSI reported by the WD 22.
  • standard deviation std
  • the variance can be obtained from the square of the standard deviation which may be computed based at least in part on measurements of SINR.
  • SINR uncertainty may be expressed as a standard deviation or as a variance.
  • the statistics unit 34 of the processing circuitry 68 of the network node 16 may select an MCS yielding a transport block size TBS MCS such that the product is maximized, where is a function of MCS, the mean ⁇ and std ⁇ .
  • BLEP ( ⁇ ) serves as a target BLEP for link adaptation while ⁇ will be the SINR used-prediction .
  • BLEP( ⁇ ) is a non-decreasing function with a.
  • the function BLEP( ⁇ ) may be defined as: where BLEP max > BLEP min and ⁇ max min are selected design parameters.
  • BLEP max and a max are upper limits on BLEP and std. In principle, any BLEP max ⁇ 1 may work, but it may be preferable to select a MCS that does not result in incorrect decoding with very high probability. In some embodiments, std is replaced with variance, and these terms are used interchangeably except where otherwise noted.
  • CSI is reported by the WD 22 where the CSI is from a single CSI process.
  • each reported CSI may include a three-tuple ⁇ RI, PMI, CQI ⁇ where RI is the rank indicator, PMI is the precoder matrix indicator and CQI is the channel quality indicator.
  • RI is the rank indicator
  • PMI is the precoder matrix indicator
  • CQI is the channel quality indicator.
  • a SINR for corresponding rank may be determined, e.g., by a function f RI (CQI) that converts RI and CQI to a corresponding SINR measurement SINR RI CQI .
  • CQI function f RI
  • the network node 16 such as by mapping unit 32 may select a rank to be used as a base rank RI base for SINR values, and all SINR measurements are mapped to RI base - This may be achieved by selecting a function )
  • a scaling factor k RI may be applied by the mapping unit 32 as follows: where the scale factor may compensate for layer non-orthogonality and/or reduced or increased interference suppression when mapping to a different rank. For example, k RI > 1 may be selected when RI is higher than the RI base and k RI ⁇ 1 may be selected when RI is higher than RI base .
  • the function may be more generally shown a where Q is a measured channel quality value.
  • the base auto-correlation of the base SINR could be determined at discrete time differences. From the base mean p base and base std ⁇ base , a probability density function P ⁇ base , ⁇ base (*) for the base SINR may be determined. By assuming a base SINR being normally distributed, a conditional probability density function given the last observed base SINR can be determined.
  • the network node 16 may artificially inject interpolated values between t 1 and t 2 .
  • ⁇ is the smallest value for which T is needed (or smallest time between two consecutive CSI reports).
  • the network node 16, via the statistics unit 34, may inject interpolated values, where interpolated values may be determined as:
  • conditional base mean and base std representing the expected base SINR and base uncertainty at a future time instance can be determined.
  • a (conditional) mean ⁇ and std ⁇ may be determined.
  • the conditional mean ⁇ may be determined using a function that is the inverse of while ⁇ may be determined to be the same as the conditional base std.
  • older CSI reports may be excluded, weighted or filtered before determining estimates of mean and variance (or std). It may be advantageous to exclude, weight or filter the CSI reports because of changing channel and interference conditions.
  • the expected SINR and uncertainty to be estimated should be the values that prevail at the time of transmission. Therefore, in some embodiments, a time window from a selected previous time to the current time may be applied. This can be achieved by various filtering methods.
  • the mean could be a filtered mean using the filter where y n is the filtered value at the time indexed by n, and x n is the value of the estimate at the time indexed by n and where a is a filtering coefficient.
  • Embodiment 2 Determining expected SINR and SINR uncertainty from CSI from multiple CSI process
  • the WD 22 may be configured with multiple CSI processes, e.g., one for each rank. Then CSI and consequently, SINR measurements, may be obtained for each rank frequently enough to calculate statistics per rank. Thus, a rank-specific mean, std and auto-correlation may be determined which enables determination of conditional mean and std representing the expected SINR and uncertainty for each rank. In some examples, fewer CSI processes than the number of different ranks may be configured. For example, if the maximum rank is 4, then a first CSI process may be configured for reported rank of 1 or 2, while second CSI process may configured for reported rank of 3 or 4. Then, the methods in Embodiment 1 may be applied twice in parallel.
  • Embodiment 3 Determining expected SINR and SINR uncertainty for reciprocitybased beamforming
  • an SINR from a latest CSI report is determined as explained above and is referred to as SINR RI reciproc .
  • the SINR corresponding to a reported CQI is referred to as SINR RI .
  • this embodiment also may use the methods in Embodiment 1 and/or Embodiment 2 to determine a conditional mean ⁇ and std ⁇ for SINR with respect to the last reported rank (RI).
  • ⁇ and ⁇ may represent the expected SINR and uncertainty with the assumption that the transmission occurs with a pre-coder that is codebook-based.
  • the network node 16 may estimate an expected SINR and uncertainty, ⁇ reciproc and ⁇ reciproc, given a non-codebook-based pre-coder.
  • One way to determine ⁇ reciproc and ⁇ reciproc is to perform the methods of Embodiments 1 and 2 with realizations of SINR CSI taken from a random variable with a distribution having mean R and std ⁇ , and then take a sample mean and std from the realizations as an estimate for ⁇ reciproc and ⁇ reciproc
  • the above methods may still apply with the assumption that SINR RI reciproc is determined with respect to the total transmit power divided among the co-scheduled WDs.
  • the estimate of variance or standard deviation can be used even if reciprocity beamforming is not used, or even if measurements are performed on a different frequency if similar statistical properties of the channel can be assumed. Note that this estimate may only cover variations in the channel, including precoder mismatch, but not interference.
  • a correction term for ( ⁇ reciproc may be added. This correction term may be computed based on, for example, CSI-reports and/or path gain measurements and scheduling information from surrounding cells.
  • Embodiment 4 Sub-band CSI reports
  • sub-band CQI is reported by the WD 22, i.e., each CSI report comprises CQI for N sub-bands.
  • the network node 16 applies the methods in Embodiment 1 and/or Embodiment 2 for each sub-band. This means that link adaption is given N two-tuples of conditional mean and std, i.e. To perform link adaption as in Embodiment 0, link adaptation may include estimating, via the statistics unit 34, a conditional mean and std prevailing on the sub-bands used for the transmission.
  • conditional mean and std may be estimated based on the ⁇ /q, ⁇ J, i E S.
  • the SINR over a set of sub-bands is equal to the mean value of SINR over the set of sub-bands, i.e. by assuming: where /q are assumed to be in the dB-domain and
  • Embodiment 1 and/or Embodiment 2 may be extended to also perform estimation of covariance C ij between sub-band i and j.
  • c i i ⁇ 2 as is known from probability theory, given the assumptions above.
  • conditional std ⁇ for the transmission sub-bands may be determined from:
  • the complexity of determining the conditional std may be reduced by various assumptions. For example, it may be assumed that the sub-bands are un-correlated which may lead to an overestimate of ⁇ . Such assumptions may be suitable when it is important that the estimate be not under-estimated. For example, if the link adaptation is performed for a high-reliable transmission it may be more important that reliability is fulfilled than that the transmission is performed as spectrally-efficient as possible. In such cases, it may be suitable to over-estimate ⁇ to have margins to various errors such as CQI quantization errors and/or estimation inaccuracies arising from the fact that neither ⁇ i nor can be assumed to be fully stationary in time. Several other simplifications are possible. For example, ⁇ or ⁇ 2 could be determined as the mean, median, minimum or maximum over or ⁇ 2 .
  • Embodiment 5 Determination of expected SINR and SINR uncertainty for rank override
  • the concepts described herein may be embodied as a method, data processing system, computer program product and/or computer storage media storing an executable computer program. Accordingly, the concepts described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects all generally referred to herein as a “circuit” or “module.” Any process, step, action and/or functionality described herein may be performed by, and/or associated to, a corresponding module, which may be implemented in software and/or firmware and/or hardware. Furthermore, the disclosure may take the form of a computer program product on a tangible computer usable storage medium having computer program code embodied in the medium that can be executed by a computer. Any suitable tangible computer readable medium may be utilized including hard disks, CD-ROMs, electronic storage devices, optical storage devices, or magnetic storage devices.
  • the computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

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Abstract

La présente invention divulgue un procédé et un nœud de réseau permettant d'estimer une qualité de canal de liaison descendante attendue et une incertitude associée à utiliser dans une adaptation de liaison. Selon un aspect, un procédé dans un nœud de réseau inclut le mappage d'une pluralité de valeurs de qualité de canal selon une première fonction de mappage sur la base d'un rang de base et d'un second rang : la détermination d'une moyenne de base et d'une variance de base des valeurs de qualité de canal mappées ; étant donnée une première qualité de canal mappée, la détermination d'une seconde moyenne et d'une seconde variance sur la base au moins en partie de la moyenne de base, de la variance de base, de la première valeur de qualité de canal mappée et d'une autocorrélation des valeurs de qualité de canal mappées ; et le mappage de la seconde moyenne selon une seconde fonction de mappage sur la base du rang de base et d'un rang sélectionné pour déterminer une valeur estimée de qualité de canal attendue à un temps dans le futur pour le rang sélectionné.
PCT/SE2022/050601 2022-06-20 2022-06-20 Estimation de la qualité de canal de liaison descendante (dl) attendue et incertitude associée à utiliser dans une adaptation de liaison WO2023249517A1 (fr)

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Citations (6)

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EP1010288A1 (fr) * 1997-08-29 2000-06-21 Telefonaktiebolaget L M Ericsson (Publ) Procede servant a selectionner une combinaison de schemas de modulation et de codage de canaux dans un systeme numerique de telecommunications
WO2017152930A1 (fr) * 2016-03-07 2017-09-14 Telefonaktiebolaget Lm Ericsson (Publ) Adaptation de liaison radio dans des systèmes de communication
US20210036804A1 (en) * 2018-03-13 2021-02-04 Telefonaktiebolaget Lm Ericsson (Publ) Method and network node, for handling link adaption of a channel
WO2021123494A1 (fr) * 2019-12-19 2021-06-24 Nokia Technologies Oy Adaptation de lien assistée selon une probabilité d'erreur
US20220149980A1 (en) * 2019-03-18 2022-05-12 Telefonaktiebolaget Lm Ericsson (Publ) Link adaptation optimized with machine learning
US20220182175A1 (en) * 2019-03-18 2022-06-09 Telefonaktiebolaget Lm Ericsson (Publ) Link adaptation optimization with contextual bandits

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1010288A1 (fr) * 1997-08-29 2000-06-21 Telefonaktiebolaget L M Ericsson (Publ) Procede servant a selectionner une combinaison de schemas de modulation et de codage de canaux dans un systeme numerique de telecommunications
WO2017152930A1 (fr) * 2016-03-07 2017-09-14 Telefonaktiebolaget Lm Ericsson (Publ) Adaptation de liaison radio dans des systèmes de communication
US20210036804A1 (en) * 2018-03-13 2021-02-04 Telefonaktiebolaget Lm Ericsson (Publ) Method and network node, for handling link adaption of a channel
US20220149980A1 (en) * 2019-03-18 2022-05-12 Telefonaktiebolaget Lm Ericsson (Publ) Link adaptation optimized with machine learning
US20220182175A1 (en) * 2019-03-18 2022-06-09 Telefonaktiebolaget Lm Ericsson (Publ) Link adaptation optimization with contextual bandits
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